AI-Powered Personalised Travel Itinerary Planning Application (Karnataka)

Abstract

The use of AI for personalized travel planning augmentsitsefficiencywhichisaremarkableadvancedfeatureof smarttourism.Unlikepreviousplanningmethods,itenablesboth adaptive and fully independent support for users. Considering how contemporary societies place more value on individually curated travel experiences, traditional travel tools still seem to lack appropriate recommendations that are timely, relevant, and user-friendly.Toaddresstheseissues,thispaperpresentsanauto- matedtravelplanningsolutionpoweredwithever-evolvingartifi- cialintelligenceandmachinelearningtechnologiesthatadaptsto the user’s context and creates fully personalized itineraries. The systemintegratesvarioustravelerprofiles,includingsolotourists, family groups with children, and adventure seekers, by blending user’smoods,interests,travelbudgets,andtripduration.Crucial aspects include user-definable real-time profiling with Adaptive Preference Classification (APC) and behavioral-driven activity and destination selection via the Multi-Constraint Itinerary OptimizationAlgorithm(MCIOA).Withenhancedrelevanceand agilitytoimprovesystemadaptability,real-timeweatherforecasts and local events updates can be integrated. Tests show that the adaptive system has greatly enhanced planning accuracy anduser satisfaction and engagement. The system has potential for use by tourism offices, augmented and virtual reality previews, and sustainable development initiatives.

Introduction

The AI-powered Dynamic Travel Planning System is an innovative platform designed to transform travel by offering hyper-personalized, smart, and seamless itineraries that adapt in real-time to travelers’ moods, preferences, budgets, and context. Unlike traditional travel apps with static recommendations, this system uses AI to generate dynamic day-by-day plans incorporating real-time data such as local events, weather, and transit routes. It integrates APIs like Google Maps and local event databases to optimize routes and suggest activities tailored to the traveler’s energy levels and interests. Users can book transportation, accommodation, and activities within the system, removing the need for multiple third-party apps.

The platform targets individual travelers, travel agencies, businesses, and tourism boards, aiming to make trip planning intuitive, immersive, and emotionally engaging. It addresses current fragmentation in travel tools by combining AI-driven personalization, mood detection, and real-time updates into a unified, modular architecture.

Technical aspects:

• The system uses a multi-layer architecture: Presentation (ReactJS frontend), Business Logic (ASP.NET MVC backend), and Data Layer (SQL Server).

• Key functionalities include user profile management, mood-based itinerary generation, dynamic route optimization, event integration, and feedback loops for continuous improvement.

• Mood prediction is central, using rule-based or machine learning models to tailor recommendations.

• The itinerary is clustered and filtered based on user psychology and preferences, with collaborative and content-based filtering methods.

• Hardware requirements include a mid-range processor, 8GB RAM, and sufficient storage.

• Security, scalability, high performance, and a user-friendly interface are emphasized.

• The system’s design supports continuous learning via user feedback to refine recommendations over time.

Literature review and theoretical foundation:

• Cites recent studies on personalized travel recommender systems leveraging machine learning, deep learning, hybrid algorithms, and real-time contextual data.

• Emphasizes that travel planning needs to evolve beyond static recommendations to immersive, mood-aware experiences.

Conclusion

The proposed AI-powered travel itinerary generator marksa significant advancement in intelligent travel planning by combiningusercentricdesignwithadaptivemachinelearningtechniques.Unlikeconventionalitinerarytoolsthatofferstaticsuggestions,thissystempersonalizestravelplansbyinterpret- ingusermood,preferences,andcontextualfactorsthrough a combination of mood classification, clustering, and route optimization. Built upon a modular architecture that integrates a Reac-tJSfrontend,anASP.NETMVCbackend,andAPIssuch as Google Maps, the platform delivers seamless end-to-end functionality, from mood inference and preference filtering to route planning and real-time feedback integration. The use ofa structured SQL Server backend and RESTful APIs ensures data consistency, scalability, and extensibility. Beyondtechnicalrobustness,thesystemillustratesthetrans- formative role of AI in enhancing user experience, offering personalized,adaptive,andemotionallyaligneditineraries. Its potential extends to various domains such as tourism management, travel personalization engines, and smart city experiences, offering value to both end-users and service providers. Whilethecurrentimplementationoperatesinacloud-hosted environment with support for basic mood classification and English-language input, future enhancements aim to include multilingualinterfaces,dynamicre-planning,edgedeployment forofflineusability,andintegrationofgenerativemodels for richer recommendation outputs. These developments will position the system as a next-generation travel companion capableofdeliveringcontext-aware,responsive,andintelligent itineraries for diverse user needs. In conclusion, this platform demonstrates how modular architecture, AI models, and real-time data can converge to redefine the landscape of personalized travel planning, setting astrongfoundationforfutureresearchinaffectivecomputing, adaptive routing, and experiential travel design.

References

[1] Archana V, Machine Learning in Modern World, Journal of ArtificialIntelligence, Machine Learning and Neural Network, Vol. 3 No. 01(2023), Dec 2022–Jan 2023. [2] AartsE,WichertR(2009)Ambientintelligence.In:BullingerHJ(ed)Technologyguide.Springer,Berlin/Heidelberg,pp.244–249. [3] Bostrom N, Yudkowsky E (2014) The ethics of artificial intelligence.In: The Cambridge handbook of artificial intelligence. Cambridge Uni-versity Press, Cambridge, pp. 316–334. [4] Buhalis D, Harwood T, Bogicevic V, Viglia G, Beldona S, Hofacker C(2019) Technological disruptions in services: lessons from tourism andhospitality. J Serv Manag 30:484–506. [5] Ricci F, Rokach L, Shapira B (2015) Recommender systems: intro-duction and challenges. In: Recommender systems handbook. Springer,Boston, pp. 1–34. [6] D.A.JelincicandY.Mansfeld,“Applyingculturaltourismintherevital-isation and enhancement of cultural heritage: An integrative approach,”in Cultural Urban Heritage: Development, Learning and LandscapeStrategies, 2019, pp. 35–43. [7] M.H.Dewantara,S.Gardiner,andX.Jin,“Travelvlogecosystemin tourism digital marketing evolution: a narrative literature review,”Current Issues in Tourism, vol. 26, no. 19, pp. 3125–3139, 2023. [8] V.Pereira,J.J.Gupta,andS.Hussain,“Impactoftravelmotivationon tourist’s attitude toward destination: Evidence of mediating effect ofdestinationimage,”JournalofHospitality&TourismResearch,vol.46,no. 5, pp. 946–971, 2022. [9] S. Nian, M. Chen, X. Zhang, D. Li, and J. Ren, “How OutstandingUniversal Value Attractiveness and Tourism Crowding Affect Visitors’Satisfaction?,” Behavioral Sciences, vol. 13, no. 2, p. 112, 2023. [10] Artemenko, V. Pasichnyk and N. Kunanec, “E-tourism mobile location-basedhybridrecommendersystemwith contextevaluation,”2019IEEE14thInternationalConferenceonComputerSciencesandInforma-tion Technologies (CSIT), Lviv, Ukraine, 2019, pp. 114–118, doi:10.1109/STC-CSIT.2019.8929775. [11] K. Li and C. Qu, “Design and Implementation of Tourism RouteRecommendation System Based on LBS,” 2021 IEEE 5th AdvancedInformation Technology, Electronic and Automation Control Con-ference (IAEAC), Chongqing, China, 2021, pp. 2748–2751, doi:10.1109/IAEAC50856.2021.9391036. [12] E.PaskahliaGunawanandC.Tho,“DevelopmentofanApplica-tion for Tourism Route Recommendations with the Dijkstra Algo-rithm,” 2021 International Conference on Information Management andTechnology (ICIMTech), Jakarta, Indonesia, 2021, pp. 343–347, doi:10.1109/ICIMTech53080.2021.9534998.

Copyright

Copyright © 2025 Vinutha Prakash, Vanishree Arun, Jahnavi M R, Prithvi J P , Purvika S Bennur, Shree Harshini. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.